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Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model
Author:
Huigang Chen
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Mr. Alin T Mirestean
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Mr. Charalambos G Tsangarides
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Series:
IMF Working Papers
Volume/Issue:
Volume 2011: Issue 230
Language:
English
Publication Date:
01 Oct 2011
DOI:
https://doi.org/10.5089/9781463921309.001
ISBN:
9781463921309
ISSN:
1018-5941
Pages:
45
Keywords:
WP
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund
Volume 2011: Issue 230